AWS Certified AI Practitioner (AIF-C01) Cert Prep
5h 44mIntermediate2025-04-04
Authors
Pearson
Chad Smith
Course details
The AWS Certified AI Practitioner (AIF-C01) exam is intended for individuals who can effectively demonstrate overall knowledge of artificial intelligence and machine learning, generative AI technologies, and associated AWS services and tools, independent of a specific job role. Check out this course to prepare for the exam, which till test your knowledge of: AI, ML, and generative AI concepts, methods, and strategies in general and on AWS; the appropriate use of AI/ML and generative AI technologies to ask relevant questions within your organization; the correct types of AI/ML technologies to apply to specific use cases; and using AI, ML, and generative AI technologies responsibly.
Skills covered
Artificial Intelligence FoundationsAmazon Web Services (AWS)AmazonCloud ServicesCloud PlatformsArtificial Intelligence (AI)Cert PrepCloud Computing
Concepts
Introduction
- AWS Certified AI Practitioner (AIF-C01) - Introduction
Exam Guide
- Module 1 - Exam foundation introduction
- Learning objectives
- Introduction
- Target candidate description
- Exam content
- Exam question domains
Basic AI Concepts
- Module 2 - Fundamentals of AI and ML introduction
- Learning objectives
- Basic AI terminology
- Introduction to machine learning
- Introduction to deep learning
- Question breakdown, part 1
- Question breakdown, part 2
Practical Use Cases for AI
- Learning objectives
- AI patterns and anti-patterns
- ML techniques
- Real-world AI applications
- AWS-managed AI ML services
- Question breakdown, part 1
- Question breakdown, part 2
ML Development Lifecycle
- Learning objectives
- ML pipeline components
- ML model sources and deployment types
- Introduction to MLOps
- AWS ML pipeline services
- ML model performance metrics
- Question breakdown, part 1
- Question breakdown, part 2
Basic Concepts of Generative AI
- Module 3 - Fundamentals of generative AI introduction
- Learning objectives
- Basic generative AI terminology
- Generative AI use cases
- Foundation model lifecycle
- Question breakdown, part 1
- Question breakdown, part 2
Generative AI Capabilities and Limitations
- Learning objectives
- Generative AI advantages
- Generative AI disadvantages
- Model selection decision tree
- Generative AI business value and metrics
- Question breakdown, part 1
- Question breakdown, part 2
AWS Generative AI Offerings
- Learning objectives
- AWS generative AI services and features
- AWS generative AI advantages and benefits
- AWS generative AI cost tradeoffs
- Question breakdown, part 1
- Question breakdown, part 2
Foundation Model Design
- Module 4 - Applications of foundation models introduction
- Learning objectives
- Pretrained model selection criteria
- Model inference parameters
- Introduction to RAG
- Introduction to vector databases
- AWS vector database service
- Foundation model customization cost tradeoffs
- Generative AI agents
- Question breakdown, part 1
- Question breakdown, part 2
Foundation Model Performance
- Learning objectives
- Foundation model performance metrics and evaluation
- Foundation model business objective criteria
- Question breakdown, part 1
- Question breakdown, part 2
Foundation Model Training and Fine-Tuning
- Learning objectives
- Foundation model training
- Foundation model fine-tuning
- Foundation model data preparation
- Question breakdown, part 1
- Question breakdown, part 2
Prompt Engineering
- Learning objectives
- Prompt workflow
- Prompt engineering concepts
- Prompt engineering techniques
- Prompt engineering best practices
- Prompt engineering risks and limitations
- Question breakdown, part 1
- Question breakdown, part 2
Responsible AI System Development
- Module 5 - Responsible and secure AI solutions introduction
- Learning objectives
- Responsible AI features
- AWS responsible AI tools
- Responsible AI model selection practices
- Generative AI legal risks
- AI dataset characteristics
- AI bias and variance
- AWS AI bias detection tools
- Question breakdown, part 1
- Question breakdown, part 2
Transparent and Explainable AI Models
- Learning objectives
- Transparency and explainability definitions
- AWS transparency and explainability tools
- AI model safety and transparency tradeoffs
- Human-centered AI design principles
- Question breakdown, part 1
- Question breakdown, part 2
AI Security
- Learning objectives
- AWS AI security services and features
- Data citations and origin documentation
- Secure data engineering best practices
- AI security and privacy considerations
- Question breakdown, part 1
- Question breakdown, part 2
AI Governance and Compliance
- Learning objectives
- AWS governance and compliance services
- Data governance strategies
- Governance protocols and compliance standards
- Question breakdown, part 1
- Question breakdown, part 2
Conclusion
- AWS Certified AI Practitioner (AIF-C01) - Summary